Evaluating the Sensitivity of Resting-State BOLD Variability to Age and Cognition after Controlling for Motion and Cardiovascular Influences: A Network-Based Approach

Abstract Recent functional magnetic resonance imaging (fMRI) studies report that moment-to-moment variability in the BOLD signal is related to differences in age and cognition and, thus, may be sensitive to age-dependent decline. However, head motion and/or cardiovascular health (CVH) may contaminate these relationships. We evaluated relationships between resting-state BOLD variability, age, and cognition, after characterizing and controlling for motion-related and cardiovascular influences, including pulse, blood pressure, BMI, and white matter hyperintensities (WMH), in a large (N = 422) resting-state fMRI sample of cognitively normal individuals (age 43–89). We found that resting-state BOLD variability was negatively related to age and positively related to cognition after maximally controlling for head motion. Age relationships also survived correction for CVH, but were greatly reduced when correcting for WMH alone. Our results suggest that network-based machine learning analyses of resting-state BOLD variability might yield reliable, sensitive measures to characterize age-related decline across a broad range of networks. Age-related differences in resting-state BOLD variability may be largely sensitive to processes related to WMH burden.

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A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies

Cerebral Cortex ◽

10.1093/cercor/bhaa139 ◽

2020 ◽

Vol 30 (10) ◽

pp. 5544-5559 ◽

Cited By ~ 5

Author(s):

Jonathan D Power ◽

Charles J Lynch ◽

Babatunde Adeyemo ◽

Steven E Petersen

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Critical Event ◽

Head Motion ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Decay Properties ◽

Signal Decay ◽

Biological Variables ◽

Related Approach

Abstract This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like “Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties”. Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.

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Aging effect on head motion: A Machine Learning study on resting state fMRI data

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2021.109084 ◽

2021 ◽

Vol 352 ◽

pp. 109084

Author(s):

Valeria Saccà ◽

Alessia Sarica ◽

Andrea Quattrone ◽

Federico Rocca ◽

Aldo Quattrone ◽

...

Keyword(s):

Machine Learning ◽

Resting State ◽

Aging Effect ◽

Resting State Fmri ◽

Fmri Data ◽

Head Motion ◽

Learning Study

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Lower functional hippocampal redundancy in mild cognitive impairment

Translational Psychiatry ◽

10.1038/s41398-020-01166-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Stephanie Langella ◽

◽

Muhammad Usman Sadiq ◽

Peter J. Mucha ◽

Kelly S. Giovanello ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Resting State ◽

Memory Performance ◽

Potential Mechanism ◽

Network Efficiency ◽

Cognitively Normal ◽

Normal Individuals ◽

The Relationship

AbstractWith an increasing prevalence of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) in response to an aging population, it is critical to identify and understand neuroprotective mechanisms against cognitive decline. One potential mechanism is redundancy: the existence of duplicate elements within a system that provide alternative functionality in case of failure. As the hippocampus is one of the earliest sites affected by AD pathology, we hypothesized that functional hippocampal redundancy is protective against cognitive decline. We compared hippocampal functional redundancy derived from resting-state functional MRI networks in cognitively normal older adults, with individuals with early and late MCI, as well as the relationship between redundancy and cognition. Posterior hippocampal redundancy was reduced between cognitively normal and MCI groups, plateauing across early and late MCI. Higher hippocampal redundancy was related to better memory performance only for cognitively normal individuals. Critically, functional hippocampal redundancy did not come at the expense of network efficiency. Our results provide support that hippocampal redundancy protects against cognitive decline in aging.

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Neuroimaging Anomalies in Community-Dwelling Asymptomatic Adults With Very Early-Stage White Matter Hyperintensity

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.715434 ◽

2021 ◽

Vol 13 ◽

Author(s):

Shuai Guan ◽

Xiangyu Kong ◽

Shifei Duan ◽

Qingguo Ren ◽

Zhaodi Huang ◽

...

Keyword(s):

White Matter ◽

Functional Mri ◽

Resting State ◽

Early Stage ◽

Structural Mri ◽

Resting State Fmri ◽

Community Dwelling ◽

White Matter Hyperintensity ◽

Age Related ◽

Comparative Results

White matter hyperintensity (WMH) is common in healthy adults in their 60s and can be seen as early as in their 30s and 40s. Alterations in the brain structural and functional profiles in adults with WMH have been repeatedly studied but with a focus on late-stage WMH. To date, structural and functional MRI profiles during the very early stage of WMH remain largely unexplored. To address this, we investigated multimodal MRI (structural, diffusion, and resting-state functional MRI) profiles of community-dwelling asymptomatic adults with very early-stage WMH relative to age-, sex-, and education-matched non-WMH controls. The comparative results showed significant age-related and age-independent changes in structural MRI-based morphometric measures and resting-state fMRI-based measures in a set of specific gray matter (GM) regions but no global white matter changes. The observed structural and functional anomalies in specific GM regions in community-dwelling asymptomatic adults with very early-stage WMH provide novel data regarding very early-stage WMH and enhance understanding of the pathogenesis of WMH.

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Age-related changes in the association of resting-state fMRI signal variability and global functional connectivity in non-demented healthy people

Psychiatry Research ◽

10.1016/j.psychres.2020.113257 ◽

2020 ◽

Vol 291 ◽

pp. 113257

Author(s):

Wanqing Xie ◽

Chung-Kang Peng ◽

Jihong Shen ◽

Ching-Po Lin ◽

Shih-Jen Tsai ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Healthy People ◽

Fmri Signal ◽

Age Related ◽

Signal Variability ◽

Age Related Changes

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IC-P-196: Decreased resting state functional connectivity at baseline predicts subjective memory complaints after 1 year in cognitively normal individuals and individuals with mild cognitive impairment

Alzheimer s & Dementia ◽

10.1016/j.jalz.2013.05.195 ◽

2013 ◽

Vol 9 ◽

pp. P110-P110 ◽

Cited By ~ 1

Author(s):

Patrizia Vannini ◽

Sarah Wigman ◽

Andrew Ward ◽

Rebecca Amariglio ◽

Gad Marshall ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Functional Connectivity ◽

Resting State ◽

Subjective Memory Complaints ◽

Resting State Functional Connectivity ◽

Subjective Memory ◽

Cognitively Normal ◽

Memory Complaints ◽

Normal Individuals

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An age-dependent Connectivity-based computer aided diagnosis system for Autism Spectrum Disorder using Resting-state fMRI

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.103108 ◽

2022 ◽

Vol 71 ◽

pp. 103108

Author(s):

Hossein Haghighat ◽

Mitra Mirzarezaee ◽

Babak Nadjar Araabi ◽

Ali Khadem

Keyword(s):

Autism Spectrum Disorder ◽

Resting State ◽

Resting State Fmri ◽

Autism Spectrum ◽

Computer Aided Diagnosis ◽

Spectrum Disorder ◽

Diagnosis System ◽

Computer Aided ◽

Age Dependent ◽

Aided Diagnosis

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Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI

Journal of Applied Mathematics ◽

10.1155/2013/935154 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 142

Author(s):

Hang Joon Jo ◽

Stephen J. Gotts ◽

Richard C. Reynolds ◽

Peter A. Bandettini ◽

Alex Martin ◽

...

Keyword(s):

Resting State ◽

Correlation Coefficients ◽

Resting State Fmri ◽

Motion Artifacts ◽

Head Motion ◽

Time Points ◽

Global Signal ◽

Motion Time ◽

Correlation Estimate ◽

The Brain

Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We replicate the previously reported head-motion bias on correlation coefficients and then show that while motion can be the source of artifact in correlations, the distance-dependent bias is exacerbated by GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that the local white matter regressor (WMeLOCAL), a subset of ANATICOR, results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.

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